We provide efficient support for applications that aim to continuously find pairs of similar sets in rapid streams, such as Twitter streams that emit tweets as sets of words. Using a sliding window model, the top-k result changes as new sets enter the window or existing ones leave the window. Specifically, when a set arrives, it may form a new top-k result pair with any set already in the window. When a set leaves the window, all its pairings in the top-k result must be replaced with other pairs. It is therefore not sufficient to maintain the k most similar pairs since less similar pairs may become top-k pairs later. We propose SWOOP, a highly scalable stream join algorithm. Novel indexing techniques and sophisticated filters efficiently prune obsolete pairs as new sets enter the window. SWOOP incrementally maintains a provably minimal stock of similar pairs to update the top-k result at any time. Empirical studies confirm that SWOOP is able to support stream rates that are orders of magnitude faster than the rates supported by existing approaches.
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